First Results: Skill Building and Self-Acceptance as Mechanisms of Positive Personality Change
Author
Michael Krämer
1 Load packages
Show the code
library(renv)library(tidyverse)library(broom)library(labelled)library(psych)library(GPArotation)#library(devtools)#install_github("cran/multicon") # not on CRAN atmlibrary(multicon)library(correlation)library(careless)library(corrplot)library(lavaan)library(semTools)library(semPlot)library(knitr)library(ggdist)library(ggforce)library(nortest)library(lmerTest)
Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
16 39 87 287 339 340 366 391 527 609
summary(fit_cfa_neuro_curr, fit.measures =TRUE)
lavaan 0.6.15 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 3
Model Test User Model:
Test statistic 569.140
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3227.954
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.837
Tucker-Lewis Index (TLI) 0.801
Robust Comparative Fit Index (CFI) 0.837
Robust Tucker-Lewis Index (TLI) 0.801
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10673.638
Loglikelihood unrestricted model (H1) -10389.067
Akaike (AIC) 21419.275
Bayesian (BIC) 21578.101
Sample-size adjusted Bayesian (SABIC) 21463.809
Root Mean Square Error of Approximation:
RMSEA 0.125
90 Percent confidence interval - lower 0.116
90 Percent confidence interval - upper 0.135
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.125
90 Percent confidence interval - lower 0.116
90 Percent confidence interval - upper 0.135
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.059
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_curr1 =~
b05_04_ (lmb1) 0.888 0.047 18.818 0.000
b05_09_ (lmb2) 0.760 0.048 15.751 0.000
b05_14_ (lmb3) -0.855 0.051 -16.904 0.000
b05_19_ (lmb4) -0.612 0.043 -14.319 0.000
b05_24_ (lmb5) 0.800 0.048 16.784 0.000
b05_29_ (lmb6) 0.930 0.045 20.642 0.000
b05_34_ (lmb7) -0.807 0.044 -18.345 0.000
b05_39_ (lmb8) -0.922 0.048 -19.389 0.000
b05_44_ (lmb9) 0.794 0.046 17.206 0.000
b05_49_ (lm10) 0.586 0.049 11.905 0.000
b05_54_ (lm11) -0.957 0.048 -20.023 0.000
b05_59_ (lm12) -0.790 0.052 -15.257 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_04_1 (i1) 2.703 0.052 52.176 0.000
.bf05_09_1 2.871 0.051 56.336 0.000
.bf05_14_1 3.256 0.054 60.173 0.000
.bf05_19_1 3.658 0.044 82.408 0.000
.bf05_24_1 2.906 0.051 57.018 0.000
.bf05_29_1 2.865 0.051 56.646 0.000
.bf05_34_1 3.885 0.048 80.965 0.000
.bf05_39_1 3.404 0.052 65.195 0.000
.bf05_44_1 3.112 0.049 63.152 0.000
.bf05_49_1 2.355 0.050 47.242 0.000
.bf05_54_1 3.337 0.053 62.949 0.000
.bf05_59_1 3.171 0.054 58.589 0.000
neur_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_04_t1 0.846 0.054 15.600 0.000
.bf05_09_t1 1.002 0.061 16.301 0.000
.bf05_14_t1 1.053 0.066 16.036 0.000
.bf05_19_t1 0.826 0.050 16.567 0.000
.bf05_24_t1 0.943 0.059 16.115 0.000
.bf05_29_t1 0.694 0.047 14.800 0.000
.bf05_34_t1 0.751 0.048 15.706 0.000
.bf05_39_t1 0.807 0.054 14.922 0.000
.bf05_44_t1 0.849 0.054 15.733 0.000
.bf05_49_t1 1.169 0.069 16.884 0.000
.bf05_54_t1 0.795 0.054 14.757 0.000
.bf05_59_t1 1.160 0.071 16.276 0.000
neuro_curr1 1.000
Good model fit across all stages of measurement invariance (except for RMSEA). Chi^2 tests indicate that strict measurement invariance is given.
5 Confirmatory results
5.1 H1: Well-being - similarity correlations
All four psychological well-being indicators will be positively correlated with a greater similarity between current- and ideal self-ratings of personality.
To examine this at the level of overall profiles, we will compute the correlations between the psychological well-being indicators and the Fisher z transformed correlations between the facet- and item-level real-ideal self-profiles. To examine this at the level of individual traits, we will compute the correlation between psychological well-being indicators and the squared difference between current- and ideal-self rating for each Big Five trait and facet.
corrplot(cormat_profile, type ="lower", order ="original", tl.col ="black", tl.srt =10,addCoef.col ='black', number.cex =0.7, diag =FALSE) # also add numbers
Positive correlations of well-being indicators with profile similarity between current self and ideal self personality. Especially high correlation with self-esteem. High congruence of item-level and facet-level profile similarity.
corrplot(cormat_sqtraits, type ="lower", order ="original", tl.col ="black", tl.srt =10,addCoef.col ='black', number.cex =0.7, diag =FALSE) # also add numbers
Big Five facets
corrplot(cormat_sqfacets, type ="lower", order ="original", tl.col ="black", tl.srt =10,addCoef.col ='black', number.cex =0.6, diag =FALSE) # also add numbers
Here we see negative correlations of well-being indicators with squared trait- and facet-level mean-score differences between current self and ideal self personality.
5.2 H2: Well-being - latent change
Both groups will increase in all four psychological well-being indicators.
We will test the mean-level difference between baseline and follow up using a latent change model.
5.2.1 Life satisfaction
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_swls_hyp2 <-'swls_t1 =~ 1*sw06_01_t1 + lamb2*sw06_02_t1 + lamb3*sw06_03_t1 + lamb4*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + lamb2*sw06_02_t2 + lamb3*sw06_03_t2 + lamb4*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ 1 # This estimates the intercept of the change score swls_t1 ~ 1 # This estimates the intercept of swls_t1 d_swls_1 ~~ d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ res1*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ res2*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ res3*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ res4*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ res1*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ res2*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ res3*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ res4*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ m2*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ m3*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ m4*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ m2*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ m3*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ m4*1 # This estimates the intercept of X4 at T2'fit_mi_lcs_swls_hyp2 <-lavaan(mi_lcs_swls_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_swls_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Significantly higher life satisfaction at the post test, b = , p = . Those with initially higher levels of life satisfaction (at T1) change to a lesser degree.
5.2.2 Meaning in life
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_meaning_hyp2 <-'meaning_t1 =~ 1*meaning_par1_t1 + lamb2*meaning_par2_t1 + lamb3*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + lamb2*meaning_par2_t2 + lamb3*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ 1 # This estimates the intercept of the change score meaning_t1 ~ 1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ res1*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ res2*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ res3*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ res1*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ res2*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ res3*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2'fit_mi_lcs_meaning_hyp2 <-lavaan(mi_lcs_meaning_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_meaning_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Participants improved significantly in meaning in life across time, b = , p = .
5.2.3 Self-esteem
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_selfes_hyp2 <-'selfes_t1 =~ 1*selfes_par1_t1 + lamb2*selfes_par2_t1 + lamb3*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + lamb2*selfes_par2_t2 + lamb3*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ 1 # This estimates the intercept of the change score selfes_t1 ~ 1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ res1*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ res2*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ res3*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ res1*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ res2*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ res3*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2'fit_mi_lcs_selfes_hyp2 <-lavaan(mi_lcs_selfes_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_selfes_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Participants increased significantly in self-esteem between the two assessments, b = , p = .
5.2.4 Self concept clarity
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_concept_hyp2 <-'concept_t1 =~ 1*concept_par1_t1 + lamb2*concept_par2_t1 + lamb3*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + lamb2*concept_par2_t2 + lamb3*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ 1 # This estimates the intercept of the change score concept_t1 ~ 1 # This estimates the intercept of concept_t1 d_concept_1 ~~ d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ res1*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ res2*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ res3*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ res1*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ res2*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ res3*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2'fit_mi_lcs_concept_hyp2 <-lavaan(mi_lcs_concept_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_concept_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Self concept clarity improved significantly across time, b = , p = .
5.3 H3: Distance between ideal- and current-self over time
The distance between ideal- and current-self will decrease in both groups.
We will use two strategies to test this hypothesis. First, we will compute the Fisher z-transformed profile correlation between current- and ideal-self and test whether it increased across assessments. Second, we will test whether the squared difference between current- and ideal-self ratings for each Big Five trait decreased across assessments. We will test mean-level differences in profile correlations and squared differences between baseline and follow up using repeated-measures t-test.
5.3.1 Profile similarity
Reshape to wide:
Show the code
# reshape to widedf_sbsa_wide_profdiff <- df_sbsa %>%arrange(pid, time) %>%select(pid, time, profile_corr_item_z, profile_corr_facet_z, ends_with("_sqdiff")) %>%pivot_wider(names_from = time,names_sep ="_t",values_from =-c(pid, time))
profile_df_plot <- df_sbsa %>%select(pid, time, profile_corr_item_z, profile_corr_facet_z) %>%pivot_longer(-c(pid, time), names_to ="itemfacet", values_to ="corr") %>%mutate(itemfacet2 =fct_recode(itemfacet, "Item-level"="profile_corr_item_z", "Facet-level"="profile_corr_facet_z"),itemfacet2 =fct_reorder(itemfacet2, corr, .desc = F))ggplot(profile_df_plot) +aes(x =as.factor(time), y = corr) +geom_boxplot() +geom_violin(fill =NA) +facet_wrap(vars(itemfacet2)) +labs(x ="Measurement Occasion", y ="Profile correlation", title ="H3: Distance between ideal- and current-self") +theme_bw()
Significantly higher profile correlations at the second measurement occasion, both for the item-level profile correlation and the facet-level profile correlations.
For some of the facets, the distribution look very similar and differences over time are perhaps driven by outliers.
5.4 H4: Change goals and change in personality (current / ideal) in skill-building group
In the skill-building group, there will be a correlation between change goals and change in current-self ratings but not change in ideal-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level differences across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
5.4.1.1 Extraversion - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_hyp4 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_extra_curr_hyp4 <-lavaan(mi_lcs_extra_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
The correlation of the general change goal with the extraversion change score (current-self) is significantly different from zero, r = 0.163, p = 0.045.
5.4.1.2 Extraversion - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_hyp4 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_extra_ideal_hyp4 <-lavaan(mi_lcs_extra_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_specif_hyp4 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_01_t1 ~~ sb07_01_t1sb07_02_t1 ~~ sb07_02_t1sb07_03_t1 ~~ sb07_03_t1sb07_01_t1 ~ 1sb07_02_t1 ~ 1sb07_03_t1 ~ 1'fit_mi_lcs_extra_curr_specif_hyp4 <-lavaan(mi_lcs_extra_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with extraversion change score (current-self) is not significantly different from zero, r = 0.044, p = 0.708.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_specif_hyp4 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_01_t1 ~~ sb07_01_t1sb07_02_t1 ~~ sb07_02_t1sb07_03_t1 ~~ sb07_03_t1sb07_01_t1 ~ 1sb07_02_t1 ~ 1sb07_03_t1 ~ 1'fit_mi_lcs_extra_ideal_specif_hyp4 <-lavaan(mi_lcs_extra_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with extraversion change score (ideal-self) is not significantly different from zero, r = 0.096, p = 0.528.
5.4.1.5 Agreeableness - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_hyp4 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_agree_curr_hyp4 <-lavaan(mi_lcs_agree_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with agreeableness change score (current-self) is not significantly different from zero, r = -0.087, p = 0.314.
5.4.1.6 Agreeableness - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_hyp4 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_agree_ideal_hyp4 <-lavaan(mi_lcs_agree_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_specif_hyp4 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_04_t1 ~~ sb07_04_t1sb07_05_t1 ~~ sb07_05_t1sb07_06_t1 ~~ sb07_06_t1sb07_04_t1 ~ 1sb07_05_t1 ~ 1sb07_06_t1 ~ 1'fit_mi_lcs_agree_curr_specif_hyp4 <-lavaan(mi_lcs_agree_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with agreeableness change score (current-self) is not significantly different from zero, r = 0.006, p = 0.955.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_specif_hyp4 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_04_t1 ~~ sb07_04_t1sb07_05_t1 ~~ sb07_05_t1sb07_06_t1 ~~ sb07_06_t1sb07_04_t1 ~ 1sb07_05_t1 ~ 1sb07_06_t1 ~ 1'fit_mi_lcs_agree_ideal_specif_hyp4 <-lavaan(mi_lcs_agree_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with agreeableness change score (ideal-self) is not significantly different from zero, r = -0.148, p = 0.145.
5.4.1.9 Conscientiousness - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_hyp4 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_consc_curr_hyp4 <-lavaan(mi_lcs_consc_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with conscientiousness change score (current-self) is not significantly different from zero, r = 0.037, p = 0.628.
5.4.1.10 Conscientiousness - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_hyp4 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_consc_ideal_hyp4 <-lavaan(mi_lcs_consc_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_specif_hyp4 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_07_t1 ~~ sb07_07_t1sb07_08_t1 ~~ sb07_08_t1sb07_09_t1 ~~ sb07_09_t1sb07_07_t1 ~ 1sb07_08_t1 ~ 1sb07_09_t1 ~ 1'fit_mi_lcs_consc_curr_specif_hyp4 <-lavaan(mi_lcs_consc_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with conscientiousness change score (current-self) is not significantly different from zero, r = -0.009, p = 0.926.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_specif_hyp4 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_07_t1 ~~ sb07_07_t1sb07_08_t1 ~~ sb07_08_t1sb07_09_t1 ~~ sb07_09_t1sb07_07_t1 ~ 1sb07_08_t1 ~ 1sb07_09_t1 ~ 1'fit_mi_lcs_consc_ideal_specif_hyp4 <-lavaan(mi_lcs_consc_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with conscientiousness change score (ideal-self) is not significantly different from zero, r = -0.051, p = 0.546.
5.4.1.13 Neuroticism - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_hyp4 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_neuro_curr_hyp4 <-lavaan(mi_lcs_neuro_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with neuroticism change score (current-self) is not significantly different from zero, r = -0.096, p = 0.159.
5.4.1.14 Neuroticism - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_hyp4 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_neuro_ideal_hyp4 <-lavaan(mi_lcs_neuro_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_specif_hyp4 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_10_t1 ~~ sb07_10_t1sb07_11_t1 ~~ sb07_11_t1sb07_12_t1 ~~ sb07_12_t1sb07_10_t1 ~ 1sb07_11_t1 ~ 1sb07_12_t1 ~ 1'fit_mi_lcs_neuro_curr_specif_hyp4 <-lavaan(mi_lcs_neuro_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
The correlation of specific, facet-level change goals with neuroticism change score (current-self) is significantly different from zero, r = 0.229, p = 0.027.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_specif_hyp4 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_10_t1 ~~ sb07_10_t1sb07_11_t1 ~~ sb07_11_t1sb07_12_t1 ~~ sb07_12_t1sb07_10_t1 ~ 1sb07_11_t1 ~ 1sb07_12_t1 ~ 1'fit_mi_lcs_neuro_ideal_specif_hyp4 <-lavaan(mi_lcs_neuro_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with neuroticism change score (ideal-self) is not significantly different from zero, r = 0.003, p = 0.966.
5.4.1.17 Openness - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_hyp4 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_openn_curr_hyp4 <-lavaan(mi_lcs_openn_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with openness change score (current-self) is not significantly different from zero, r = -0.061, p = 0.552.
5.4.1.18 Openness - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_hyp4 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_openn_ideal_hyp4 <-lavaan(mi_lcs_openn_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_specif_hyp4 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_13_t1 ~~ sb07_13_t1sb07_14_t1 ~~ sb07_14_t1sb07_15_t1 ~~ sb07_15_t1sb07_13_t1 ~ 1sb07_14_t1 ~ 1sb07_15_t1 ~ 1'fit_mi_lcs_openn_curr_specif_hyp4 <-lavaan(mi_lcs_openn_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
The correlation of specific, facet-level change goals with openness change score (current-self) is significantly different from zero, r = 0.247, p = 0.026.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_specif_hyp4 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_13_t1 ~~ sb07_13_t1sb07_14_t1 ~~ sb07_14_t1sb07_15_t1 ~~ sb07_15_t1sb07_13_t1 ~ 1sb07_14_t1 ~ 1sb07_15_t1 ~ 1'fit_mi_lcs_openn_ideal_specif_hyp4 <-lavaan(mi_lcs_openn_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with openness change score (ideal-self) is not significantly different from zero, r = 0.097, p = 0.321.
5.4.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create template:facet_template <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 d_facet_1 ~ facet_t1 # This estimates the self-feedback parameterd_facet_1 ~~ ind_goal # estimates the covariance/correlation with change goal variableind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# loop across 2 BFI versions (combined pre&post current/ideal)for (j in5:length(bfi_versions)) { items =paste0(bfi_versions[[j]], item_nrs)# loop across 2 different goal operationalizations (sb06_01_t1 & sb07_XX_t1)for (k in1:2) {if (k==1) { goal_op ="sb06_01_t1" } else{ goal_op =paste0("sb07_", str_pad(i-5, 2, pad ="0"), "_t1") } template_filled <-str_replace_all(facet_template, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= goal_op)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')# save to environmentif (k==1) {eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp4")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp4")), facet_model_fit)) } else{eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), facet_model_fit)) } } }}
5.4.2.1 Sociability - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
The correlation of the general change goal with the sociability change score (current-self) is significantly different from zero, r = 0.162, p = 0.047.
5.4.2.2 Sociability - ideal-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with sociability change score (current-self) is not significantly different from zero, r = -0.097, p = 0.231.
The correlation of specific, facet-level change goals with the anxiety change score (ideal-self) is (barely) significantly different from zero, r = 0.228, p = 0.05.
5.4.2.5 Assertiveness - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with assertiveness change score (current-self) is not significantly different from zero, r = 0.06, p = 0.66.
Correlation of specific, facet-level change goals with assertiveness change score (ideal-self) is not significantly different from zero, r = 0.082, p = 0.42.
5.4.2.9 Energy - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with energy change score (current-self) is not significantly different from zero, r = 0.103, p = 0.226.
5.4.2.12 Energy - ideal-self: specific, facet-level change goals
Results summary (sb07_xx_t1 = trait/facet specific change goal):
Correlation of specific, facet-level change goals with energy change score (ideal-self) is not significantly different from zero, r = 0.098, p = 0.351.
5.4.2.13 Compassion - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with compassion change score (current-self) is not significantly different from zero, r = -0.103, p = 0.561.
Correlation of specific, facet-level change goals with compassion change score (ideal-self) is not significantly different from zero, r = -0.083, p = 0.468.
5.4.2.17 Respectfulness - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with respectfulness change score (current-self) is not significantly different from zero, r = 0.023, p = 0.826.
The correlation of specific, facet-level change goals with the respectfulness change score (ideal-self) is significantly different from zero, r = -0.186, p = 0.048.
5.4.2.21 Trust - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with trust change score (current-self) is not significantly different from zero, r = -0.032, p = 0.753.
Correlation of specific, facet-level change goals with organization change score (current-self) is not significantly different from zero, r = 0.11, p = 0.282.
Correlation of specific, facet-level change goals with organization change score (ideal-self) is not significantly different from zero, r = 0.103, p = 0.257.
5.4.2.29 Productiveness - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with productiveness change score (current-self) is not significantly different from zero, r = 0.06, p = 0.542.
Correlation of specific, facet-level change goals with productiveness change score (ideal-self) is not significantly different from zero, r = 0.042, p = 0.618.
5.4.2.33 Responsibility - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with responsibility change score (current-self) is not significantly different from zero, r = -0.155, p = 0.174.
Correlation of specific, facet-level change goals with responsibility change score (ideal-self) is not significantly different from zero, r = -0.012, p = 0.91.
5.4.2.37 Anxiety - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
The correlation of specific, facet-level change goals with the anxiety change score (current-self) is significantly different from zero, r = -0.267, p = 0.007.
Correlation of specific, facet-level change goals with anxiety change score (ideal-self) is not significantly different from zero, r = 0.09, p = 0.353.
5.4.2.41 Depression - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
The correlation of specific, facet-level change goals with the depression change score (current-self) is significantly different from zero, r = -0.281, p = 0.01.
Correlation of specific, facet-level change goals with the depression change score (ideal-self) is not significantly different from zero, r = -0.115, p = 0.136.
5.4.2.45 Volatility - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with volatility change score (current-self) is not significantly different from zero, r = -0.012, p = 0.898.
Correlation of specific, facet-level change goals with volatility change score (ideal-self) is not significantly different from zero, r = -0.056, p = 0.571.
5.4.2.49 Curiosity - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with curiosity change score (current-self) is not significantly different from zero, r = -0.112, p = 0.532.
Correlation of specific, facet-level change goals with curiosity change score (ideal-self) is not significantly different from zero, r = 0.126, p = 0.712.
5.4.2.53 Aesthetic - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with aesthetic change score (current-self) is not significantly different from zero, r = 0.164, p = 0.051.
Correlation of specific, facet-level change goals with aesthetic change score (ideal-self) is not significantly different from zero, r = 0.176, p = 0.176.
5.4.2.57 Imagination - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with imagination change score (current-self) is not significantly different from zero, r = 0.076, p = 0.404.
Correlation of specific, facet-level change goals with imagination change score (ideal-self) is not significantly different from zero, r = -0.025, p = 0.757.
Results summary across the Big Five traits: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[1:20, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
extraversion
current
general
0.052
0.163
2.006
0.045
extraversion
ideal
general
0.035
0.135
1.673
0.094
extraversion
current
specific
0.008
0.044
0.374
0.708
extraversion
ideal
specific
0.014
0.096
0.630
0.528
agreeableness
current
general
-0.018
-0.087
-1.006
0.314
agreeableness
ideal
general
-0.002
-0.009
-0.098
0.922
agreeableness
current
specific
0.001
0.006
0.056
0.955
agreeableness
ideal
specific
-0.035
-0.148
-1.459
0.145
conscientiousness
current
general
0.011
0.037
0.484
0.628
conscientiousness
ideal
general
0.011
0.040
0.447
0.655
conscientiousness
current
specific
-0.003
-0.009
-0.093
0.926
conscientiousness
ideal
specific
-0.016
-0.051
-0.603
0.546
neuroticism
current
general
-0.045
-0.096
-1.407
0.159
neuroticism
ideal
general
-0.027
-0.084
-1.033
0.302
neuroticism
current
specific
0.121
0.229
2.218
0.027
neuroticism
ideal
specific
0.001
0.003
0.043
0.966
openness
current
general
-0.012
-0.061
-0.594
0.552
openness
ideal
general
-0.028
-0.115
-1.154
0.249
openness
current
specific
0.029
0.247
2.221
0.026
openness
ideal
specific
0.015
0.097
0.993
0.321
Three covariances/correlations that significantly differ from zero:
- Changes in current-level extraversion covary with the general change goal.
- Changes in current-level neuroticism covary with the specific trait goals (latent factor of the three N facets).
- Changes in current-level openness covary with the specific trait goals (latent factor of the three O facets).
Results summary across the Big Five facets: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[21:80, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
sociability
current
general
0.081
0.162
1.983
0.047
sociability
ideal
general
0.042
0.187
1.540
0.124
sociability
current
specific
-0.058
-0.097
-1.197
0.231
sociability
ideal
specific
0.062
0.228
1.959
0.050
assertiveness
current
general
0.010
0.049
0.328
0.743
assertiveness
ideal
general
0.037
0.201
1.876
0.061
assertiveness
current
specific
0.014
0.060
0.440
0.660
assertiveness
ideal
specific
0.018
0.082
0.807
0.420
energy
current
general
-0.018
-0.076
-0.808
0.419
energy
ideal
general
-0.031
-0.119
-1.136
0.256
energy
current
specific
0.031
0.103
1.212
0.226
energy
ideal
specific
0.031
0.098
0.932
0.351
compassion
current
general
0.018
0.102
0.697
0.486
compassion
ideal
general
0.015
0.040
0.351
0.725
compassion
current
specific
-0.025
-0.103
-0.581
0.561
compassion
ideal
specific
-0.042
-0.083
-0.725
0.468
respectfulness
current
general
-0.040
-0.139
-1.500
0.134
respectfulness
ideal
general
-0.006
-0.026
-0.273
0.785
respectfulness
current
specific
0.009
0.023
0.220
0.826
respectfulness
ideal
specific
-0.057
-0.186
-1.974
0.048
trust
current
general
-0.020
-0.063
-0.636
0.525
trust
ideal
general
0.002
0.011
0.115
0.908
trust
current
specific
-0.013
-0.032
-0.314
0.753
trust
ideal
specific
0.016
0.054
0.515
0.607
organization
current
general
-0.067
-0.141
-1.580
0.114
organization
ideal
general
-0.023
-0.073
-0.751
0.452
organization
current
specific
0.077
0.110
1.076
0.282
organization
ideal
specific
0.048
0.103
1.133
0.257
productiveness
current
general
0.004
0.012
0.139
0.890
productiveness
ideal
general
0.004
0.014
0.142
0.887
productiveness
current
specific
0.026
0.060
0.611
0.542
productiveness
ideal
specific
0.016
0.042
0.498
0.618
responsibility
current
general
-0.036
-0.179
-1.647
0.100
responsibility
ideal
general
0.003
0.010
0.095
0.925
responsibility
current
specific
-0.047
-0.155
-1.359
0.174
responsibility
ideal
specific
-0.005
-0.012
-0.113
0.910
anxiety
current
general
0.057
0.109
1.412
0.158
anxiety
ideal
general
0.010
0.069
0.629
0.530
anxiety
current
specific
-0.218
-0.267
-2.707
0.007
anxiety
ideal
specific
0.021
0.090
0.930
0.353
depression
current
general
0.021
0.058
0.794
0.427
depression
ideal
general
0.035
0.117
1.281
0.200
depression
current
specific
-0.172
-0.281
-2.591
0.010
depression
ideal
specific
-0.054
-0.115
-1.492
0.136
volatility
current
general
0.017
0.036
0.423
0.672
volatility
ideal
general
-0.043
-0.143
-1.373
0.170
volatility
current
specific
-0.007
-0.012
-0.128
0.898
volatility
ideal
specific
-0.022
-0.056
-0.567
0.571
curiosity
current
general
-0.007
-0.043
-0.258
0.797
curiosity
ideal
general
-0.010
-0.129
-0.385
0.700
curiosity
current
specific
-0.023
-0.112
-0.625
0.532
curiosity
ideal
specific
0.012
0.126
0.369
0.712
aesthetic
current
general
-0.001
-0.039
-0.466
0.642
aesthetic
ideal
general
-0.008
-0.100
-0.961
0.337
aesthetic
current
specific
0.006
0.164
1.953
0.051
aesthetic
ideal
specific
0.020
0.176
1.352
0.176
imagination
current
general
0.009
0.025
0.280
0.780
imagination
ideal
general
0.013
0.035
0.414
0.679
imagination
current
specific
0.040
0.076
0.834
0.404
imagination
ideal
specific
-0.013
-0.025
-0.309
0.757
Looking at the facets, we see five covariances that significantly differ from zero:
- For sociability, changes in current-level covary with the general change goal and changes in ideal-level with the specific facet change goal (both effects barely significant).
- Matching the effects from neuroticism above, we find that changes in current-level anxiety and depression covary with the respective specific facet change goal.
- Further, changes in ideal-level respectfulness covary with the specific facet change goal (small effect that is barely significant; in the right direction, though -> minus sign is because a reverse-keyed item was used as reference indicator).
5.5 H5: Acceptance goals and change in personality (current / ideal) in self-acceptance group
In the self-acceptance group, there will be a correlation between acceptance goals and change in ideal-self ratings but not change in current-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
5.5.1.1 Extraversion - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_hyp5 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_extra_curr_hyp5 <-lavaan(mi_lcs_extra_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of general acceptance goal with the extraversion change score (current-self) is significantly different from zero, r = 0.14, p = 0.023.
5.5.1.2 Extraversion - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_hyp5 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_extra_ideal_hyp5 <-lavaan(mi_lcs_extra_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_specif_hyp5 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_01_t1 ~~ sa07_01_t1sa07_02_t1 ~~ sa07_02_t1sa07_03_t1 ~~ sa07_03_t1sa07_01_t1 ~ 1sa07_02_t1 ~ 1sa07_03_t1 ~ 1'fit_mi_lcs_extra_curr_specif_hyp5 <-lavaan(mi_lcs_extra_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with extraversion change score (current-self) is not significantly different from zero, r = 0.082, p = 0.46.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_specif_hyp5 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_01_t1 ~~ sa07_01_t1sa07_02_t1 ~~ sa07_02_t1sa07_03_t1 ~~ sa07_03_t1sa07_01_t1 ~ 1sa07_02_t1 ~ 1sa07_03_t1 ~ 1'fit_mi_lcs_extra_ideal_specif_hyp5 <-lavaan(mi_lcs_extra_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with extraversion change score (ideal-self) is not significantly different from zero, r = -0.179, p = 0.123.
5.5.1.5 Agreeableness - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_hyp5 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_agree_curr_hyp5 <-lavaan(mi_lcs_agree_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with agreeableness change score (current-self) is not significantly different from zero, r = 0.15, p = 0.122.
5.5.1.6 Agreeableness - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_hyp5 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_agree_ideal_hyp5 <-lavaan(mi_lcs_agree_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_specif_hyp5 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_04_t1 ~~ sa07_04_t1sa07_05_t1 ~~ sa07_05_t1sa07_06_t1 ~~ sa07_06_t1sa07_04_t1 ~ 1sa07_05_t1 ~ 1sa07_06_t1 ~ 1'fit_mi_lcs_agree_curr_specif_hyp5 <-lavaan(mi_lcs_agree_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with agreeableness change score (current-self) is not significantly different from zero, r = 0.011, p = 0.916.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_specif_hyp5 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_04_t1 ~~ sa07_04_t1sa07_05_t1 ~~ sa07_05_t1sa07_06_t1 ~~ sa07_06_t1sa07_04_t1 ~ 1sa07_05_t1 ~ 1sa07_06_t1 ~ 1'fit_mi_lcs_agree_ideal_specif_hyp5 <-lavaan(mi_lcs_agree_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with agreeableness change score (ideal-self) is not significantly different from zero, r = -0.057, p = 0.583.
5.5.1.9 Conscientiousness - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_hyp5 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_consc_curr_hyp5 <-lavaan(mi_lcs_consc_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with conscientiousness change score (current-self) is not significantly different from zero, r = -0.008, p = 0.899.
5.5.1.10 Conscientiousness - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_hyp5 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_consc_ideal_hyp5 <-lavaan(mi_lcs_consc_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with conscientiousness change score (ideal-self) is not significantly different from zero, r = -0.08, p = 0.235.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_specif_hyp5 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_07_t1 ~~ sa07_07_t1sa07_08_t1 ~~ sa07_08_t1sa07_09_t1 ~~ sa07_09_t1sa07_07_t1 ~ 1sa07_08_t1 ~ 1sa07_09_t1 ~ 1'fit_mi_lcs_consc_curr_specif_hyp5 <-lavaan(mi_lcs_consc_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the conscientiousness change score (current-self) is significantly different from zero, r = -0.3, p = 0.009.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_specif_hyp5 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_07_t1 ~~ sa07_07_t1sa07_08_t1 ~~ sa07_08_t1sa07_09_t1 ~~ sa07_09_t1sa07_07_t1 ~ 1sa07_08_t1 ~ 1sa07_09_t1 ~ 1'fit_mi_lcs_consc_ideal_specif_hyp5 <-lavaan(mi_lcs_consc_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the conscientiousness change score (ideal-self) is significantly different from zero, r = -0.12, p = 0.137.
5.5.1.13 Neuroticism - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_hyp5 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_neuro_curr_hyp5 <-lavaan(mi_lcs_neuro_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with neuroticism change score (current-self) is not significantly different from zero, r = -0.131, p = 0.085.
5.5.1.14 Neuroticism - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_hyp5 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_neuro_ideal_hyp5 <-lavaan(mi_lcs_neuro_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_specif_hyp5 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_10_t1 ~~ sa07_10_t1sa07_11_t1 ~~ sa07_11_t1sa07_12_t1 ~~ sa07_12_t1sa07_10_t1 ~ 1sa07_11_t1 ~ 1sa07_12_t1 ~ 1'fit_mi_lcs_neuro_curr_specif_hyp5 <-lavaan(mi_lcs_neuro_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with neuroticism change score (current-self) is significantly different from zero, r = 0.097, p = 0.297.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_specif_hyp5 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_10_t1 ~~ sa07_10_t1sa07_11_t1 ~~ sa07_11_t1sa07_12_t1 ~~ sa07_12_t1sa07_10_t1 ~ 1sa07_11_t1 ~ 1sa07_12_t1 ~ 1'fit_mi_lcs_neuro_ideal_specif_hyp5 <-lavaan(mi_lcs_neuro_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the neuroticism change score (ideal-self) is significantly different from zero, r = 0.183, p = 0.009.
5.5.1.17 Openness - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_hyp5 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_openn_curr_hyp5 <-lavaan(mi_lcs_openn_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with openness change score (current-self) is not significantly different from zero, r = 0.013, p = 0.862.
5.5.1.18 Openness - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_hyp5 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_openn_ideal_hyp5 <-lavaan(mi_lcs_openn_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='WLSMV', fixed.x=FALSE, ordered="sa06_01_t1")# This model did not converge properly (when adding the 'sa06_01_t1' goal variable). Declaring 'sa06_01_t1' as an # ordered variable and using the WLSMV estimator (sadly without FIML) worked in the end. Results # https://lavaan.ugent.be/tutorial/cat.htmlsummary(fit_mi_lcs_openn_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_specif_hyp5 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_13_t1 ~~ sa07_13_t1sa07_14_t1 ~~ sa07_14_t1sa07_15_t1 ~~ sa07_15_t1sa07_13_t1 ~ 1sa07_14_t1 ~ 1sa07_15_t1 ~ 1'fit_mi_lcs_openn_curr_specif_hyp5 <-lavaan(mi_lcs_openn_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with openness change score (current-self) is not significantly different from zero, r = -0.151, p = 0.092.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_specif_hyp5 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_13_t1 ~~ sa07_13_t1sa07_14_t1 ~~ sa07_14_t1sa07_15_t1 ~~ sa07_15_t1sa07_13_t1 ~ 1sa07_14_t1 ~ 1sa07_15_t1 ~ 1'fit_mi_lcs_openn_ideal_specif_hyp5 <-lavaan(mi_lcs_openn_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='WLSMV', fixed.x=FALSE, ordered=c("sa07_13_t1", "sa07_14_t1", "sa07_15_t1"))# This model did not converge properly (when adding the 'sa06_01_t1' goal variable). # Declaring c("sa07_13_t1", "sa07_14_t1", "sa07_15_t1") as # ordered variables and using the WLSMV estimator (sadly without FIML) worked in the end. Results # https://lavaan.ugent.be/tutorial/cat.htmlsummary(fit_mi_lcs_openn_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the openness change score (ideal-self) is significantly different from zero, r = -0.22, p = 0.036.
5.5.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create template:facet_template <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 d_facet_1 ~ facet_t1 # This estimates the self-feedback parameterd_facet_1 ~~ ind_goal # estimates the covariance/correlation with acceptance goal variableind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# loop across 2 BFI versions (combined pre&post current/ideal)for (j in5:length(bfi_versions)) { items =paste0(bfi_versions[[j]], item_nrs)# loop across 2 different goal operationalizations (sa06_01_t1 & sa07_XX_t1)for (k in1:2) {if (k==1) { goal_op ="sa06_01_t1" } else{ goal_op =paste0("sa07_", str_pad(i-5, 2, pad ="0"), "_t1") } template_filled <-str_replace_all(facet_template, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= goal_op)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')# save to environmentif (k==1) {eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp5")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp5")), facet_model_fit)) } else{eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), facet_model_fit)) } } }}
5.5.2.1 Sociability - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of the general acceptance goal with the sociability change score (current-self) is significantly different from zero, r = 0.177, p = 0.025.
5.5.2.2 Sociability - ideal-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with sociability change score (current-self) is not significantly different from zero, r = -0.024, p = 0.797.
Correlation of specific, facet-level acceptance goals with anxiety change score (ideal-self) is not significantly different from zero, r = -0.133, p = 0.324.
5.5.2.5 Assertiveness - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with assertiveness change score (current-self) is not significantly different from zero, r = -0.01, p = 0.916.
Correlation of specific, facet-level acceptance goals with assertiveness change score (ideal-self) is not significantly different from zero, r = -0.082, p = 0.392.
5.5.2.9 Energy - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with energy change score (current-self) is not significantly different from zero, r = 0.125, p = 0.179.
5.5.2.12 Energy - ideal-self: specific, facet-level acceptance goals
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with energy change score (ideal-self) is not significantly different from zero, r = 0.096, p = 0.452.
5.5.2.13 Compassion - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with compassion change score (current-self) is not significantly different from zero, r = 0.035, p = 0.685.
Correlation of specific, facet-level acceptance goals with compassion change score (ideal-self) is not significantly different from zero, r = -0.146, p = 0.182.
5.5.2.17 Respectfulness - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with respectfulness change score (current-self) is not significantly different from zero, r = 0.008, p = 0.935.
Correlation of specific, facet-level acceptance goals with respectfulness change score (ideal-self) is not significantly different from zero, r = -0.107, p = 0.311.
5.5.2.21 Trust - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with trust change score (current-self) is not significantly different from zero, r = 0.162, p = 0.063.
Correlation of specific, facet-level acceptance goals with trust change score (ideal-self) is not significantly different from zero, r = 0.093, p = 0.322.
5.5.2.25 Organization - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of specific, facet-level acceptance goals with the organization change score (current-self) is significantly different from zero, r = 0.255, p = 0.016.
Correlation of specific, facet-level acceptance goals with organization change score (ideal-self) is not significantly different from zero, r = 0.114, p = 0.246.
5.5.2.29 Productiveness - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with productiveness change score (current-self) is not significantly different from zero, r = -0.039, p = 0.625.
5.5.2.30 Productiveness - ideal-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with productiveness change score (current-self) is not significantly different from zero, r = 0.183, p = 0.111.
Correlation of specific, facet-level acceptance goals with productiveness change score (ideal-self) is not significantly different from zero, r = -0.013, p = 0.892.
5.5.2.33 Responsibility - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of specific, facet-level acceptance goals with the responsibility change score (current-self) is significantly different from zero, r = -0.471, p = 0.001.
Correlation of specific, facet-level acceptance goals with responsibility change score (ideal-self) is not significantly different from zero, r = -0.092, p = 0.326.
5.5.2.37 Anxiety - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with anxiety change score (current-self) is not significantly different from zero, r = -0.037, p = 0.728.
Correlation of specific, facet-level acceptance goals with anxiety change score (ideal-self) is not significantly different from zero, r = 0.025, p = 0.852.
5.5.2.41 Depression - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with depression change score (current-self) is not significantly different from zero, r = -0.116, p = 0.282.
Correlation of specific, facet-level acceptance goals with depression change score (ideal-self) is not significantly different from zero, r = -0.076, p = 0.493.
5.5.2.45 Volatility - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with volatility change score (current-self) is not significantly different from zero, r = -0.024, p = 0.795.
Correlation of specific, facet-level acceptance goals with volatility change score (ideal-self) is not significantly different from zero, r = -0.036, p = 0.687.
5.5.2.49 Curiosity - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of specific, facet-level acceptance goals with the curiosity change score (current-self) is significantly different from zero, r = -0.264, p = 0.013.
Correlation of specific, facet-level acceptance goals with curiosity change score (ideal-self) is not significantly different from zero, r = -0.236, p = 0.062.
5.5.2.53 Aesthetic - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with aesthetic change score (current-self) is not significantly different from zero, r = -0.008, p = 0.921.
Correlation of specific, facet-level acceptance goals with aesthetic change score (ideal-self) is not significantly different from zero, r = -0.119, p = 0.441.
5.5.2.57 Imagination - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with imagination change score (current-self) is not significantly different from zero, r = -0.145, p = 0.153.
Correlation of specific, facet-level acceptance goals with imagination change score (ideal-self) is not significantly different from zero, r = -0.008, p = 0.942.
Results summary across the Big Five traits: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[1:20, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
extraversion
current
general
0.047
0.140
2.273
0.023
extraversion
ideal
general
0.018
0.081
1.133
0.257
extraversion
current
specific
0.024
0.082
0.739
0.460
extraversion
ideal
specific
-0.035
-0.179
-1.543
0.123
agreeableness
current
general
0.029
0.150
1.548
0.122
agreeableness
ideal
general
0.001
0.003
0.037
0.970
agreeableness
current
specific
0.002
0.011
0.105
0.916
agreeableness
ideal
specific
-0.013
-0.057
-0.548
0.583
conscientiousness
current
general
-0.003
-0.008
-0.126
0.899
conscientiousness
ideal
general
-0.022
-0.080
-1.189
0.235
conscientiousness
current
specific
-0.115
-0.300
-2.610
0.009
conscientiousness
ideal
specific
-0.040
-0.120
-1.485
0.137
neuroticism
current
general
-0.052
-0.131
-1.725
0.085
neuroticism
ideal
general
-0.017
-0.060
-0.802
0.422
neuroticism
current
specific
0.035
0.097
1.042
0.297
neuroticism
ideal
specific
0.045
0.183
2.622
0.009
openness
current
general
0.003
0.013
0.174
0.862
openness
ideal
general
0.050
0.187
1.799
0.072
openness
current
specific
-0.030
-0.151
-1.683
0.092
openness
ideal
specific
-0.036
-0.220
-2.098
0.036
Five covariances significantly differ from zero:
changes in current-level extraversion covary with the general acceptance goal
changes in current-level conscientiousness covary with the specific acceptance goals (latent factor of the three C facets) -> unexpected direction of the effect!
changes in ideal-level neuroticism covary with the specific acceptance goals (latent factor of the three N facets)
changes in ideal-level openness covary with the specific acceptance goals (latent factor of the three O facets)
Results summary across the Big Five facets: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[21:80, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
sociability
current
general
0.062
0.177
2.239
0.025
sociability
ideal
general
-0.005
-0.020
-0.239
0.811
sociability
current
specific
-0.012
-0.024
-0.257
0.797
sociability
ideal
specific
-0.045
-0.133
-0.986
0.324
assertiveness
current
general
0.044
0.144
1.738
0.082
assertiveness
ideal
general
0.017
0.100
1.059
0.290
assertiveness
current
specific
-0.004
-0.010
-0.106
0.916
assertiveness
ideal
specific
-0.021
-0.082
-0.856
0.392
energy
current
general
-0.013
-0.050
-0.684
0.494
energy
ideal
general
-0.014
-0.144
-1.038
0.299
energy
current
specific
0.047
0.125
1.344
0.179
energy
ideal
specific
0.013
0.096
0.752
0.452
compassion
current
general
0.074
0.181
1.704
0.088
compassion
ideal
general
0.026
0.075
0.709
0.478
compassion
current
specific
0.019
0.035
0.406
0.685
compassion
ideal
specific
-0.067
-0.146
-1.334
0.182
respectfulness
current
general
0.006
0.027
0.270
0.787
respectfulness
ideal
general
-0.010
-0.052
-0.517
0.605
respectfulness
current
specific
0.003
0.008
0.081
0.935
respectfulness
ideal
specific
-0.028
-0.107
-1.012
0.311
trust
current
general
0.001
0.003
0.031
0.976
trust
ideal
general
-0.030
-0.120
-1.293
0.196
trust
current
specific
0.062
0.162
1.858
0.063
trust
ideal
specific
0.034
0.093
0.991
0.322
organization
current
general
0.019
0.048
0.596
0.551
organization
ideal
general
0.021
0.107
1.126
0.260
organization
current
specific
0.167
0.255
2.410
0.016
organization
ideal
specific
0.036
0.114
1.159
0.246
productiveness
current
general
-0.013
-0.039
-0.489
0.625
productiveness
ideal
general
-0.005
-0.027
-0.288
0.774
productiveness
current
specific
0.099
0.183
1.595
0.111
productiveness
ideal
specific
-0.004
-0.013
-0.135
0.892
responsibility
current
general
0.012
0.051
0.445
0.657
responsibility
ideal
general
-0.009
-0.027
-0.278
0.781
responsibility
current
specific
-0.178
-0.471
-3.430
0.001
responsibility
ideal
specific
-0.045
-0.092
-0.983
0.326
anxiety
current
general
0.026
0.064
0.646
0.518
anxiety
ideal
general
0.019
0.071
0.811
0.418
anxiety
current
specific
-0.023
-0.037
-0.348
0.728
anxiety
ideal
specific
0.011
0.025
0.187
0.852
depression
current
general
0.021
0.058
0.744
0.457
depression
ideal
general
0.000
0.002
0.021
0.983
depression
current
specific
-0.067
-0.116
-1.077
0.282
depression
ideal
specific
-0.015
-0.076
-0.685
0.493
volatility
current
general
-0.069
-0.145
-1.712
0.087
volatility
ideal
general
-0.049
-0.149
-1.673
0.094
volatility
current
specific
-0.016
-0.024
-0.260
0.795
volatility
ideal
specific
-0.016
-0.036
-0.403
0.687
curiosity
current
general
0.006
0.018
0.200
0.842
curiosity
ideal
general
0.015
0.081
0.778
0.437
curiosity
current
specific
-0.119
-0.264
-2.492
0.013
curiosity
ideal
specific
-0.064
-0.236
-1.863
0.062
aesthetic
current
general
-0.003
-0.097
-0.699
0.485
aesthetic
ideal
general
0.002
0.050
0.586
0.558
aesthetic
current
specific
0.000
-0.008
-0.100
0.921
aesthetic
ideal
specific
-0.007
-0.119
-0.770
0.441
imagination
current
general
0.020
0.067
0.663
0.507
imagination
ideal
general
-0.029
-0.138
-1.246
0.213
imagination
current
specific
-0.068
-0.145
-1.428
0.153
imagination
ideal
specific
-0.002
-0.008
-0.073
0.942
Looking at the facets, we find four covariances that significantly differ from zero (relatively unsystematic across facets / current-ideal / goal dimension):
- Changes in current-level sociability covary with the general acceptance goal.
- Further, changes in current-level organization, responsibility, and curiosity covary with the respective specific facet acceptance goal.
5.6 H6: Desire to change and frequency of skill-building behaviors as moderators of change in personality in skill-building group
Desire to change and frequency of skill-building behaviors measured at the follow-up assessment will be positively related to change in current-self ratings in the skill-building group.
To test this hypothesis, we will estimate the mean-level difference in current trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to change on a given big five domain or facet. The second will indicate their frequency of skill-building behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
# create templates:# 1st, for facet-specific change goalstrait_template_mod_goal <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingsgoals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ goals # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change scoregoals ~ 0*1 # This fixes the intercept of the moderator to 0goals ~~ goals # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind_goal_1 ~~ ind_goal_1ind_goal_2 ~~ ind_goal_2ind_goal_3 ~~ ind_goal_3ind_goal_1 ~ 1ind_goal_2 ~ 1ind_goal_3 ~ 1'trait_facets_nrs <-list(a1 =c(1:3), b2 =c(4:6), c3 =c(7:9), d4 =c(10:12), e5 =c(13:15)) # matching facet nrs to traits # loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead! mod_names =paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad ="0"), "_t1") template_filled <-str_replace_all(trait_template_mod_goal, c("trait"= short_name,"ind01"=paste0(short_name, "_curr_par1"), "ind02"=paste0(short_name, "_curr_par2"), "ind03"=paste0(short_name, "_curr_par3"), "ind_goal_1"= mod_names[1], "ind_goal_2"= mod_names[2], "ind_goal_3"= mod_names[3])) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), trait_model_fit))} # 2nd, for frequency of skill-building behaviortrait_template_mod_frequ <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsfrequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ frequ # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb04_01_t2 ~~ sb04_01_t2sb04_02_t2 ~~ sb04_02_t2sb04_03_t2 ~~ sb04_03_t2sb04_01_t2 ~ 1sb04_02_t2 ~ 1sb04_03_t2 ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_mod_frequ, c("trait"= short_name,"ind01"=paste0(short_name, "_curr_par1"), "ind02"=paste0(short_name, "_curr_par2"), "ind03"=paste0(short_name, "_curr_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), trait_model_fit))}
5.6.1.1 Extraversion: specific, facet-level change goals as moderator of change
Results summary (goals = trait/facet specific change goal):
The moderation effect of specific, facet-level change goals with the extraversion change score (current-self) is not significantly different from zero, b = 0.07, p = 0.348.
5.6.1.2 Extraversion: frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of the frequency of skill-building behaviors with the extraversion change score (current-self) is not significantly different from zero, b = 0.083, p = 0.063.
5.6.1.3 Agreeableness: specific, facet-level change goals as moderator of change
Results summary (goals = trait/facet specific change goal):
The moderation effect of specific, facet-level change goals with the agreeableness change score (current-self) is not significantly different from zero, b = 0.018, p = 0.509.
5.6.1.4 Agreeableness: frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of specific, facet-level change goals with the conscientiousness change score (current-self) is not significantly different from zero, b = 0.028, p = 0.343.
5.6.1.6 Conscientiousness: frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of specific, facet-level change goals with the neuroticism change score (current-self) is not significantly different from zero, b = 0.071, p = 0.21.
5.6.1.8 Neuroticism: frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of the frequency of skill-building behaviors with the neuroticism change score (current-self) is not significantly different from zero, b = -0.069, p = 0.229.
5.6.1.9 Openness: specific, facet-level change goals as moderator of change
Results summary (goals = trait/facet specific change goal):
The frequency of skill-building behaviors significantly moderates changes in openness (current-self), b = 0.079, p = 0.03.
5.6.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific change goalfacet_template_mod_goal <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[5]], item_nrs) mod_name =paste0("sb07_", str_pad(i-5, 2, pad ="0"), "_t1") template_filled <-str_replace_all(facet_template_mod_goal, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= mod_name)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), facet_model_fit))} # 2nd, for frequency of skill-building behaviorfacet_template_mod_frequ <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderatorfacet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ frequ # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2sb04_01_t2 ~~ sb04_01_t2sb04_02_t2 ~~ sb04_02_t2sb04_03_t2 ~~ sb04_03_t2sb04_01_t2 ~ 1sb04_02_t2 ~ 1sb04_03_t2 ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[5]], item_nrs) template_filled <-str_replace_all(facet_template_mod_frequ, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), facet_model_fit))}
5.6.2.1 Sociability - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the sociability change score (current-self) is not significantly different from zero, b = -0.013, p = 0.708.
5.6.2.2 Sociability - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with sociability change score (ideal-self) is not significantly different from zero, b = 0.055, p = 0.416.
5.6.2.3 Assertiveness - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the assertiveness change score (current-self) is not significantly different from zero, b = 0.039, p = 0.162.
5.6.2.4 Assertiveness - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the assertiveness change score (ideal-self) is not significantly different from zero, b = 0.058, p = 0.213.
5.6.2.5 Energy - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the energy change score (current-self) is not significantly different from zero, b = 0.003, p = 0.86.
5.6.2.6 Energy - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the energy change score (ideal-self) is not significantly different from zero, b = -0.066, p = 0.12.
5.6.2.7 Compassion - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the compassion change score (current-self) is not significantly different from zero, b = 0.009, p = 0.719.
5.6.2.8 Compassion - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the facet-specific change goal with the respectfulness change score (current-self) is not significantly different from zero, b = 0.031, p = 0.209.
5.6.2.10 Respectfulness - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the facet-specific change goal with the trust change score (current-self) is not significantly different from zero, b = -0.014, p = 0.583.
5.6.2.12 Trust - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the skill-building behaviors with the trust change score (current-self) is not significantly different from zero, b = -0.102, p = 0.06.
5.6.2.13 Organization - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the organization change score (current-self) is not significantly different from zero, b = -0.019, p = 0.624.
5.6.2.14 Organization - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the organization change score (current-self) is not significantly different from zero, b = -0.122, p = 0.057.
5.6.2.15 Productiveness - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the productiveness change score (current-self) is not significantly different from zero, b = -0.029, p = 0.314.
5.6.2.16 Productiveness - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the facet-specific change goal with the responsibility change score (current-self) is not significantly different from zero, b = 0.001, p = 0.935.
5.6.2.18 Responsibility - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the responsibility change score (ideal-self) is not significantly different from zero, b = 0.032, p = 0.342.
5.6.2.19 Anxiety - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the anxiety change score (current-self) is not significantly different from zero, b = -0.056, p = 0.177.
5.6.2.20 Anxiety - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the anxiety change score (ideal-self) is not significantly different from zero, b = 0.068, p = 0.377.
5.6.2.21 Depression - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the depression change score (current-self) is not significantly different from zero, b = -0.022, p = 0.459.
5.6.2.22 Depression - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the depression change score (ideal-self) is not significantly different from zero, b = 0.021, p = 0.657.
5.6.2.23 Volatility - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the volatility change score (current-self) is not significantly different from zero, b = -0.028, p = 0.41.
5.6.2.24 Volatility - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the volatility change score (ideal-self) is significantly different from zero, b = -0.067, p = 0.302.
5.6.2.25 Curiosity - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the curiosity change score (current-self) is not significantly different from zero, b = 0.005, p = 0.841.
5.6.2.26 Curiosity - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the curiosity change score (ideal-self) is not significantly different from zero, b = 0.095, p = 0.065.
5.6.2.27 Aesthetic - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the aesthetic change score (current-self) is not significantly different from zero, b = 0.003, p = 0.059.
5.6.2.28 Aesthetic - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the aesthetic change score (ideal-self) is not significantly different from zero, b = 0.006, p = 0.051.
5.6.2.29 Imagination - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the imagination change score (current-self) is not significantly different from zero, b = 0.036, p = 0.149.
5.6.2.30 Imagination - frequency of skill-building behaviors as moderator of change
Results summary (frequ = frequency of skill-building behavior):
The moderation effect of the frequency of skill-building behaviors with the imagination change score (ideal-self) is not significantly different from zero, b = 0.077, p = 0.224.
Results summary across the Big Five traits: trait-specific change goals (goals) and frequency of skill-building behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp6[1:10, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
extraversion
goals
0.070
0.138
0.939
0.348
extraversion
frequency
0.083
0.177
1.862
0.063
agreeableness
goals
0.018
0.072
0.661
0.509
agreeableness
frequency
0.088
0.290
2.771
0.006
conscientiousness
goals
0.028
0.105
0.949
0.343
conscientiousness
frequency
0.093
0.206
2.460
0.014
neuroticism
goals
0.071
0.166
1.253
0.210
neuroticism
frequency
-0.069
-0.099
-1.203
0.229
openness
goals
0.106
0.354
2.799
0.005
openness
frequency
0.079
0.291
2.167
0.030
Four moderator effects significantly differ from zero:
changes in current-level agreeableness are moderated by the frequency of skill-building behaviors
changes in current-level conscientiousness are moderated by the frequency of skill-building behaviors
changes in current-level openness are moderated by the trait-specific change goals
changes in current-level openness are moderated by the frequency of skill-building behaviors
Results summary across the Big Five facets: trait-specific change goals (goals) and frequency of skill-building behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp6[11:40, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
sociability
goals
-0.013
-0.030
-0.374
0.708
sociability
frequency
0.055
0.074
0.813
0.416
assertiveness
goals
0.039
0.221
1.397
0.162
assertiveness
frequency
0.058
0.205
1.245
0.213
energy
goals
0.003
0.015
0.177
0.860
energy
frequency
-0.066
-0.185
-1.556
0.120
compassion
goals
0.009
0.059
0.360
0.719
compassion
frequency
0.114
0.409
2.436
0.015
respectfulness
goals
0.031
0.130
1.255
0.209
respectfulness
frequency
0.081
0.183
1.982
0.047
trust
goals
-0.014
-0.055
-0.550
0.583
trust
frequency
-0.102
-0.226
-1.883
0.060
organization
goals
-0.019
-0.053
-0.490
0.624
organization
frequency
-0.122
-0.170
-1.907
0.057
productiveness
goals
-0.029
-0.107
-1.006
0.314
productiveness
frequency
-0.125
-0.253
-2.605
0.009
responsibility
goals
0.001
0.010
0.082
0.935
responsibility
frequency
0.032
0.113
0.951
0.342
anxiety
goals
-0.056
-0.153
-1.351
0.177
anxiety
frequency
0.068
0.086
0.883
0.377
depression
goals
-0.022
-0.095
-0.741
0.459
depression
frequency
0.021
0.040
0.444
0.657
volatility
goals
-0.028
-0.073
-0.824
0.410
volatility
frequency
-0.067
-0.096
-1.033
0.302
curiosity
goals
0.005
0.040
0.201
0.841
curiosity
frequency
0.095
0.392
1.846
0.065
aesthetic
goals
0.003
0.155
1.885
0.059
aesthetic
frequency
0.006
0.180
1.948
0.051
imagination
goals
0.036
0.130
1.442
0.149
imagination
frequency
0.077
0.138
1.216
0.224
Looking at the facets, we find five moderator effects that significantly differ from zero:
Within agreeableness, we find the effect for the frequency of skill-building behaviors from above represented in the two facets compassion and respectfulness.
The effect for conscientiousness is represented in one of the three facets, productiveness.
However, the effect seen above for openness is mirrored in none of the facets.
5.7 H7: Desire to change and frequency of self-acceptance behaviors as moderators of change in personality in self-acceptance group
Desire to change and frequency of self-acceptance behaviors measured at the follow-up assessment will be positively related to change in ideal-self ratings in the self-acceptance group.
To test this hypothesis, we will estimate the mean-level difference in ideal trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to accept themselves on a given big five domain or facet. The second will indicate their frequency of self-acceptance behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
# create templates:# 1st, for facet-specific acceptance goalstrait_template_mod_goal_accept <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingsgoals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ goals # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change scoregoals ~ 0*1 # This fixes the intercept of the moderator to 0goals ~~ goals # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind_goal_1 ~~ ind_goal_1ind_goal_2 ~~ ind_goal_2ind_goal_3 ~~ ind_goal_3ind_goal_1 ~ 1ind_goal_2 ~ 1ind_goal_3 ~ 1'trait_facets_nrs <-list(a1 =c(1:3), b2 =c(4:6), c3 =c(7:9), d4 =c(10:12), e5 =c(13:15)) # matching facet nrs to traits # loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal (6 = ideal)# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead! mod_names =paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad ="0"), "_t1") template_filled <-str_replace_all(trait_template_mod_goal_accept, c("trait"= short_name,"ind01"=paste0(short_name, "_ideal_par1"), "ind02"=paste0(short_name, "_ideal_par2"), "ind03"=paste0(short_name, "_ideal_par3"),"ind_goal_1"= mod_names[1], "ind_goal_2"= mod_names[2], "ind_goal_3"= mod_names[3])) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), trait_model_fit))} # 2nd, for frequency of self-acceptance behaviortrait_template_mod_frequ_accept <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsfrequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ frequ # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa04_01_t2 ~~ sa04_01_t2sa04_02_t2 ~~ sa04_02_t2sa04_03_t2 ~~ sa04_03_t2sa04_01_t2 ~ 1sa04_02_t2 ~ 1sa04_03_t2 ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_mod_frequ_accept, c("trait"= short_name,"ind01"=paste0(short_name, "_ideal_par1"), "ind02"=paste0(short_name, "_ideal_par2"), "ind03"=paste0(short_name, "_ideal_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), trait_model_fit))}
5.7.1.1 Extraversion: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the extraversion change score (ideal-self) is not significantly different from zero, b = -0.051, p = 0.13.
5.7.1.2 Extraversion: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of the frequency of self-acceptance behaviors with the extraversion change score (ideal-self) is not significantly different from zero, b = 0.037, p = 0.24.
5.7.1.3 Agreeableness: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the agreeableness change score (ideal-self) is not significantly different from zero, b = -0.02, p = 0.583.
5.7.1.4 Agreeableness: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of the frequency of self-acceptance behaviors with the agreeableness change score (ideal-self) is not significantly different from zero, b = 0.053, p = 0.178.
5.7.1.5 Conscientiousness: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the conscientiousness change score (ideal-self) is not significantly different from zero, b = -0.026, p = 0.223.
5.7.1.6 Conscientiousness: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of frequency of self-acceptance behaviors with the conscientiousness change score (ideal-self) is not significantly different from zero, b = 0.064, p = 0.111.
5.7.1.7 Neuroticism: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the neuroticism change score (ideal-self) is not significantly different from zero, b = 0.073, p = 0.099.
5.7.1.8 Neuroticism: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of the frequency of self-acceptance behaviors with the neuroticism change score (ideal-self) is not significantly different from zero, b = -0.049, p = 0.187.
5.7.1.9 Openness: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the openness change score (ideal-self) is not significantly different from zero, b = 0, p = 1.
5.7.1.10 Openness: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of skill building behavior):
The moderation effect of frequency of self-acceptance behaviors with the openness change score (ideal-self) is not significantly different from zero, b = 0.022, p = 0.53.
5.7.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific acceptance goalfacet_template_mod_goal_accept <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal items =paste0(bfi_versions[[6]], item_nrs) mod_name =paste0("sa07_", str_pad(i-5, 2, pad ="0"), "_t1") template_filled <-str_replace_all(facet_template_mod_goal_accept, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= mod_name)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), facet_model_fit))} # 2nd, for frequency of self-acceptance behaviorfacet_template_mod_frequ_accept <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderatorfacet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ frequ # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2sa04_01_t2 ~~ sa04_01_t2sa04_02_t2 ~~ sa04_02_t2sa04_03_t2 ~~ sa04_03_t2sa04_01_t2 ~ 1sa04_02_t2 ~ 1sa04_03_t2 ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal items =paste0(bfi_versions[[6]], item_nrs) template_filled <-str_replace_all(facet_template_mod_frequ_accept, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), facet_model_fit))}
5.7.2.1 Sociability - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the sociability change score (ideal-self) is not significantly different from zero, b = -0.03, p = 0.232.
5.7.2.2 Sociability - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with sociability change score (ideal-self) is not significantly different from zero, b = -0.002, p = 0.959.
5.7.2.3 Assertiveness - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the assertiveness change score (ideal-self) is not significantly different from zero, b = -0.014, p = 0.279.
5.7.2.4 Assertiveness - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the assertiveness change score (ideal-self) is not significantly different from zero, b = 0.056, p = 0.077.
5.7.2.5 Energy - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the energy change score (ideal-self) is not significantly different from zero, b = 0.009, p = 0.364.
5.7.2.6 Energy - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the energy change score (ideal-self) is not significantly different from zero, b = -0.022, p = 0.367.
5.7.2.7 Compassion - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the compassion change score (ideal-self) is not significantly different from zero, b = -0.037, p = 0.216.
5.7.2.8 Compassion - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the compassion change score (ideal-self) is not significantly different from zero, b = -0.036, p = 0.655.
5.7.2.9 Respectfulness - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the respectfulness change score (ideal-self) is not significantly different from zero, b = -0.008, p = 0.639.
5.7.2.10 Respectfulness - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the respectfulness change score (ideal-self) is not significantly different from zero, b = 0.028, p = 0.435.
5.7.2.11 Trust - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the trust change score (ideal-self) is not significantly different from zero, b = 0.024, p = 0.194.
5.7.2.12 Trust - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the trust change score (ideal-self) is not significantly different from zero, b = -0.07, p = 0.124.
5.7.2.13 Organization - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the organization change score (ideal-self) is not significantly different from zero, b = 0.011, p = 0.462.
5.7.2.14 Organization - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the facet-specific acceptance goal with the productiveness change score (ideal-self) is not significantly different from zero, b = -0.006, p = 0.653.
5.7.2.16 Productiveness - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the productiveness change score (ideal-self) is not significantly different from zero, b = -0.061, p = 0.105.
5.7.2.17 Responsibility - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the responsibility change score (ideal-self) is not significantly different from zero, b = -0.017, p = 0.467.
5.7.2.18 Responsibility - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the responsibility change score (ideal-self) is not significantly different from zero, b = 0.053, p = 0.335.
5.7.2.19 Anxiety - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the anxiety change score (ideal-self) is not significantly different from zero, b = 0.004, p = 0.886.
5.7.2.20 Anxiety - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the anxiety change score (ideal-self) is not significantly different from zero, b = 0.026, p = 0.661.
5.7.2.21 Depression - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the depression change score (ideal-self) is not significantly different from zero, b = 0.001, p = 0.95.
5.7.2.22 Depression - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the depression change score (ideal-self) is not significantly different from zero, b = 0.029, p = 0.214.
5.7.2.23 Volatility - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the volatility change score (ideal-self) is not significantly different from zero, b = -0.008, p = 0.761.
5.7.2.24 Volatility - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the volatility change score (ideal-self) is significantly different from zero, b = -0.073, p = 0.145.
5.7.2.25 Curiosity - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the curiosity change score (ideal-self) is not significantly different from zero, b = -0.022, p = 0.272.
5.7.2.26 Curiosity - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the curiosity change score (ideal-self) is not significantly different from zero, b = 0.016, p = 0.691.
5.7.2.27 Aesthetic - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the aesthetic change score (ideal-self) is not significantly different from zero, b = -0.004, p = 0.435.
5.7.2.28 Aesthetic - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the aesthetic change score (ideal-self) is not significantly different from zero, b = 0.011, p = 0.409.
5.7.2.29 Imagination - specific, facet-level acceptance goal as moderator of change
Results summary (*sa07_$$_t1* = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the imagination change score (ideal-self) is not significantly different from zero, b = 0.006, p = 0.708.
5.7.2.30 Imagination - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the imagination change score (ideal-self) is not significantly different from zero, b = -0.016, p = 0.741.
Results summary across the Big Five traits: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp7[1:10, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
extraversion
goals
-0.051
-0.164
-1.515
0.130
extraversion
frequency
0.037
0.108
1.176
0.240
agreeableness
goals
-0.020
-0.055
-0.549
0.583
agreeableness
frequency
0.053
0.141
1.345
0.178
conscientiousness
goals
-0.026
-0.096
-1.218
0.223
conscientiousness
frequency
0.064
0.157
1.595
0.111
neuroticism
goals
0.073
0.178
1.649
0.099
neuroticism
frequency
-0.049
-0.118
-1.321
0.187
openness
goals
0.000
0.000
-0.001
1.000
openness
frequency
0.022
0.061
0.628
0.530
No moderator effects that significantly differ from zero.
Results summary across the Big Five facets: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp7[11:40, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
sociability
goals
-0.030
-0.156
-1.195
0.232
sociability
frequency
-0.002
-0.005
-0.051
0.959
assertiveness
goals
-0.014
-0.098
-1.083
0.279
assertiveness
frequency
0.056
0.208
1.769
0.077
energy
goals
0.009
0.116
0.908
0.364
energy
frequency
-0.022
-0.147
-0.902
0.367
compassion
goals
-0.037
-0.138
-1.238
0.216
compassion
frequency
-0.036
-0.075
-0.447
0.655
respectfulness
goals
-0.008
-0.052
-0.470
0.639
respectfulness
frequency
0.028
0.106
0.781
0.435
trust
goals
0.024
0.125
1.298
0.194
trust
frequency
-0.070
-0.197
-1.537
0.124
organization
goals
0.011
0.073
0.735
0.462
organization
frequency
-0.091
-0.333
-2.323
0.020
productiveness
goals
-0.006
-0.043
-0.450
0.653
productiveness
frequency
-0.061
-0.230
-1.620
0.105
responsibility
goals
-0.017
-0.067
-0.728
0.467
responsibility
frequency
0.053
0.112
0.965
0.335
anxiety
goals
0.004
0.019
0.143
0.886
anxiety
frequency
0.026
0.063
0.439
0.661
depression
goals
0.001
0.008
0.062
0.950
depression
frequency
0.029
0.165
1.244
0.214
volatility
goals
-0.008
-0.029
-0.305
0.761
volatility
frequency
-0.073
-0.173
-1.456
0.145
curiosity
goals
-0.022
-0.148
-1.098
0.272
curiosity
frequency
0.016
0.061
0.398
0.691
aesthetic
goals
-0.004
-0.120
-0.781
0.435
aesthetic
frequency
0.011
0.191
0.826
0.409
imagination
goals
0.006
0.041
0.375
0.708
imagination
frequency
-0.016
-0.052
-0.331
0.741
Only one moderator effect that is significantly different from zero:
The effect for conscientiousness (frequency of self-acceptance behaviors) is only represented within the organization facet.
6.1 Differences in change across experimental groups (a)
We will explore a) whether change in psychological well-being indicators as well as the difference between real- and ideal- self will differ across groups at follow-up.
6.1.1 Well-being change: differences across groups
6.1.1.1 Life satisfaction
Life satisfaction: fitting multi-group models
Show the code
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_swls_group_config <-'swls_t1 =~ 1*sw06_01_t1 + c("lamb2a", "lamb2b")*sw06_02_t1 + c("lamb3a", "lamb3b")*sw06_03_t1 + c("lamb4a", "lamb4b")*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + c("lamb2a", "lamb2b")*sw06_02_t2 + c("lamb3a", "lamb3b")*sw06_03_t2 + c("lamb4a", "lamb4b")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1 d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2'lcs_swls_group_config <-sem(mi_lcs_swls_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_swls_group_weak <-'swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1 d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2'lcs_swls_group_weak <-sem(mi_lcs_swls_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_swls_group_strong <-'swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score swls_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of swls_t1 d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T2'lcs_swls_group_strong <-sem(mi_lcs_swls_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Life satisfaction: results
# model comparison tests for measurement invariancelavTestLRT(lcs_swls_group_config, lcs_swls_group_weak, lcs_swls_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_swls_group_config 50 12581 12749 93.676
lcs_swls_group_weak 53 12581 12736 99.658 6.2538 3 0.09989 .
lcs_swls_group_strong 58 12575 12708 103.548 3.8751 5 0.56754
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# show model with varying latent change parameters # -> key parameter is "d_swls_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)kable(broom::tidy(lcs_swls_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_swls_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_swls_1 ~1
d_int_a
0.799
1.283
5.517
0
d_swls_1 ~1
d_int_b
0.700
1.051
5.060
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_swls_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_swls_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_swls_1 ~1
d_int
0.752
1.219
7.526
0
d_swls_1 ~1
d_int
0.752
1.120
7.526
0
Slightly more positive change in life satisfaction in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_swls_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_meaning_group_config <-'meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2a", "lamb2b")*meaning_par2_t1 + c("lamb3a", "lamb3b")*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2a", "lamb2b")*meaning_par2_t2 + c("lamb3a", "lamb3b")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_meaning_group_config <-sem(mi_lcs_meaning_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_meaning_group_weak <-'meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_meaning_group_weak <-sem(mi_lcs_meaning_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_meaning_group_strong <-'meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score meaning_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2'lcs_meaning_group_strong <-sem(mi_lcs_meaning_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Meaning in life: results
# model comparison tests for measurement invariancelavTestLRT(lcs_meaning_group_config, lcs_meaning_group_weak, lcs_meaning_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_meaning_group_config 24 9277.2 9410.0 36.570
lcs_meaning_group_weak 26 9273.3 9397.3 36.734 0.1454 2 0.9299
lcs_meaning_group_strong 30 9269.0 9375.3 40.431 3.5145 4 0.4757
# show model with varying latent change parameters # -> key parameter is "d_meaning_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)kable(broom::tidy(lcs_meaning_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_meaning_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_meaning_1 ~1
d_int_a
1.328
1.644
7.171
0
d_meaning_1 ~1
d_int_b
1.381
1.698
7.117
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_meaning_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_meaning_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_meaning_1 ~1
d_int
1.353
1.664
10.113
0
d_meaning_1 ~1
d_int
1.353
1.671
10.113
0
Slightly more positive change in meaning in life in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_meaning_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_selfes_group_config <-'selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2a", "lamb2b")*selfes_par2_t1 + c("lamb3a", "lamb3b")*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2a", "lamb2b")*selfes_par2_t2 + c("lamb3a", "lamb3b")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_selfes_group_config <-sem(mi_lcs_selfes_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_selfes_group_weak <-'selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_selfes_group_weak <-sem(mi_lcs_selfes_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_selfes_group_strong <-'selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score selfes_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2'lcs_selfes_group_strong <-sem(mi_lcs_selfes_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Meaning in life: results
# model comparison tests for measurement invariancelavTestLRT(lcs_selfes_group_config, lcs_selfes_group_weak, lcs_selfes_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_selfes_group_config 24 6491.3 6624.1 21.406
lcs_selfes_group_weak 26 6489.5 6613.4 23.582 2.2801 2 0.3198
lcs_selfes_group_strong 30 6487.1 6593.4 29.256 5.4364 4 0.2454
# show model with varying latent change parameters # -> key parameter is "d_selfes_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)kable(broom::tidy(lcs_selfes_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_selfes_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_selfes_1 ~1
d_int_a
1.100
2.035
7.109
0
d_selfes_1 ~1
d_int_b
0.933
1.783
6.481
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_selfes_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_selfes_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_selfes_1 ~1
d_int
1.027
1.924
9.608
0
d_selfes_1 ~1
d_int
1.027
1.931
9.608
0
Slightly more positive change in self-esteem in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_selfes_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_concept_group_config <-'concept_t1 =~ 1*concept_par1_t1 + c("lamb2a", "lamb2b")*concept_par2_t1 + c("lamb3a", "lamb3b")*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + c("lamb2a", "lamb2b")*concept_par2_t2 + c("lamb3a", "lamb3b")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1 d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_concept_group_config <-sem(mi_lcs_concept_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_concept_group_weak <-'concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1 d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_concept_group_weak <-sem(mi_lcs_concept_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_concept_group_strong <-'concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score concept_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of concept_t1 d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2'lcs_concept_group_strong <-sem(mi_lcs_concept_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Meaning in life: results
# model comparison tests for measurement invariancelavTestLRT(lcs_concept_group_config, lcs_concept_group_weak, lcs_concept_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_concept_group_config 24 7539.6 7672.4 26.360
lcs_concept_group_weak 26 7536.2 7660.1 26.953 0.51746 2 0.7720
lcs_concept_group_strong 30 7530.2 7636.4 28.930 1.99827 4 0.7361
# show model with varying latent change parameters # -> key parameter is "d_concept_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)kable(broom::tidy(lcs_concept_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_concept_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_concept_1 ~1
d_int_a
1.148
1.974
7.532
0
d_concept_1 ~1
d_int_b
1.036
1.906
6.734
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_concept_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_concept_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_concept_1 ~1
d_int
1.095
1.902
10.081
0
d_concept_1 ~1
d_int
1.095
1.998
10.081
0
Slightly more positive change in self-concept clarity in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_concept_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
6.1.2 Current- and ideal-self personality differences across groups
Profile correlations by group and measurement occasion (mixed effects models) - results:
df_sbsa <- df_sbsa %>%mutate(time_d = time -1)psych::describeBy(df_sbsa$profile_corr_item_z, list(df_sbsa$rando, df_sbsa$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 298 0.38 0.49 0.37 0.37 0.47 -1.05 1.86 2.91 0.19 0.26 0.03
------------------------------------------------------------
: Skill-Building
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 311 0.38 0.47 0.35 0.35 0.44 -0.71 2.65 3.36 0.78 1.94 0.03
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 300 0.54 0.57 0.5 0.52 0.54 -0.9 3.44 4.34 0.92 3.17 0.03
------------------------------------------------------------
: Skill-Building
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 315 0.56 0.49 0.52 0.55 0.48 -0.77 2.65 3.42 0.42 0.91 0.03
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 298 0.28 0.67 0.21 0.26 0.64 -1.5 2.56 4.06 0.31 0.25 0.04
------------------------------------------------------------
: Skill-Building
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 311 0.28 0.65 0.23 0.25 0.65 -1.06 2.65 3.71 0.61 0.56 0.04
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 300 0.48 0.76 0.45 0.45 0.74 -1.17 3.94 5.11 0.66 1.56 0.04
------------------------------------------------------------
: Skill-Building
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 315 0.52 0.7 0.49 0.5 0.77 -1.22 2.65 3.87 0.23 -0.2 0.04
With both conceptualizations of the similarity / difference of current- and ideal-level personality (profile correlations / squared differences), we find no significant effects of group (at T1) or of interaction effects of group by measurement occasion (at T2).
6.2 Personal project dimensions (b)
We will explore b) whether the extent of change/acceptance is related to personal project dimension variables.
For now, I use the personal project dimension variables assessed at T1:
Skill-building group: “How important is it for you to change your personality?”
Skill-building group: “How difficult is it for you to work on changing your personality?”
Self-acceptance group: “How important is it for you to accept your personality?”
Self-acceptance group: “How difficult is it for you to work on accepting your personality?”
They were, however, also assessed at T2:
Skill-building group: “During this study, how important was it for you to change your personality?”
Skill-building group: “During this study, how difficult was it for you to work on changing your personality?”
Self-acceptance group: “During this study, how important was it for you to accept your personality?”
Self-acceptance group: “During this study, how difficult was it for you to work on accepting your personality?”
6.2.1 Personal project dimensions as moderators of change in personality in skill-building group
Reshape and split data set by intervention group:
Show the code
df_sbsa_wide_pers_sb_ppd <- df_sbsa %>%filter(rando=="Skill-Building") %>%arrange(pid, time) %>%select(pid, time, starts_with(c("sb01"))) %>%# Personal project dimensions - skill buildingpivot_wider(names_from = time,names_sep ="_t",values_from =c(starts_with(c("sb01")))) %>%select(-c(sb01_01_t2, sb01_02_t2))# colnames(df_sbsa_wide_pers_sb_ppd)group_assign <- df_sbsa %>%select(pid, rando) %>%unique()df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers %>%left_join(group_assign) %>%filter(rando=="Skill-Building") %>%select(-rando) %>%left_join(df_sbsa_wide_pers_sb_ppd)# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers_sb_ppd %>%mutate(ppd =rowMeans(across(c(sb01_01_t1, sb01_02_t1)), na.rm=T))
6.2.1.1 Big Five traits
Run models for all traits with a template & loop:
Show the code
# create templates:# 1st, for facet-specific change goalstrait_template_ppd_skill <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingstrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ ppd # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ppd ~~ ppdppd ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_ppd_skill, c("trait"= short_name,"ind01"=paste0(short_name, "_curr_par1"), "ind02"=paste0(short_name, "_curr_par2"), "ind03"=paste0(short_name, "_curr_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), trait_model_fit))}
6.2.1.1.1 Extraversion: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the agreeableness change score (current-self) is not significantly different from zero, b = -0.014, p = 0.438.
6.2.1.1.3 Conscientiousness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the conscientiousness change score (current-self) is not significantly different from zero, b = 0.007, p = 0.764.
6.2.1.1.4 Neuroticism: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the neuroticism change score (current-self) is not significantly different from zero, b = -0.051, p = 0.132.
6.2.1.1.5 Openness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the openness change score (current-self) is not significantly different from zero, b = 0.004, p = 0.838.
6.2.1.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific change goalfacet_template_ppd_skill <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ppd # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ppd ~~ ppdppd ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[5]], item_nrs) template_filled <-str_replace_all(facet_template_ppd_skill, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), facet_model_fit))}
6.2.1.2.1 Sociability - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the sociability change score (current-self) is not significantly different from zero, b = 0.069, p = 0.148.
6.2.1.2.2 Assertiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the assertiveness change score (current-self) is not significantly different from zero, b = 0.059, p = 0.06.
6.2.1.2.3 Energy - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the energy change score (current-self) is not significantly different from zero, b = -0.018, p = 0.483.
6.2.1.2.4 Compassion - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the compassion change score (current-self) is not significantly different from zero, b = 0.04, p = 0.219.
6.2.1.2.5 Respectfulness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the respectfulness change score (current-self) is not significantly different from zero, b = -0.039, p = 0.121.
6.2.1.2.6 Trust - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the trust change score (current-self) is not significantly different from zero, b = -0.003, p = 0.938.
6.2.1.2.7 Organization - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the organization change score (current-self) is not significantly different from zero, b = -0.042, p = 0.3.
6.2.1.2.8 Productiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the productiveness change score (current-self) is not significantly different from zero, b = -0.033, p = 0.347.
6.2.1.2.9 Responsibility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the responsibility change score (current-self) is not significantly different from zero, b = -0.038, p = 0.108.
6.2.1.2.10 Anxiety - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the anxiety change score (current-self) is not significantly different from zero, b = 0.046, p = 0.286.
6.2.1.2.11 Depression - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the depression change score (current-self) is not significantly different from zero, b = 0.056, p = 0.078.
6.2.1.2.12 Volatility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the volatility change score (current-self) is not significantly different from zero, b = -0.014, p = 0.737.
6.2.1.2.13 Curiosity - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the curiosity change score (current-self) is not significantly different from zero, b = -0.025, p = 0.365.
6.2.1.2.14 Aesthetic - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the aesthetic change score (current-self) is not significantly different from zero, b = -0.001, p = 0.611.
6.2.1.2.15 Imagination - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the imagination change score (current-self) is not significantly different from zero, b = 0.026, p = 0.47.
Results summary across the Big Five traits: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_skill[1:5, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
extraversion
ppd
0.056
0.158
2.082
0.037
agreeableness
ppd
-0.014
-0.061
-0.776
0.438
conscientiousness
ppd
0.007
0.022
0.300
0.764
neuroticism
ppd
-0.051
-0.095
-1.507
0.132
openness
ppd
0.004
0.020
0.204
0.838
Only one moderator effect significantly differs from zero:
changes in current-level extraversion are moderated by the personal project dimensions
Results summary across the Big Five facets: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_skill[6:20, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
sociability
ppd
0.069
0.120
1.446
0.148
assertiveness
ppd
0.059
0.272
1.882
0.060
energy
ppd
-0.018
-0.065
-0.702
0.483
compassion
ppd
0.040
0.176
1.230
0.219
respectfulness
ppd
-0.039
-0.114
-1.551
0.121
trust
ppd
-0.003
-0.007
-0.078
0.938
organization
ppd
-0.042
-0.075
-1.036
0.300
productiveness
ppd
-0.033
-0.087
-0.940
0.347
responsibility
ppd
-0.038
-0.177
-1.605
0.108
anxiety
ppd
0.046
0.074
1.067
0.286
depression
ppd
0.056
0.135
1.760
0.078
volatility
ppd
-0.014
-0.025
-0.336
0.737
curiosity
ppd
-0.025
-0.138
-0.906
0.365
aesthetic
ppd
-0.001
-0.041
-0.509
0.611
imagination
ppd
0.026
0.061
0.722
0.470
No significant moderation effects of personal project dimensions on the facet-level.
6.2.2 Personal project dimensions as moderators of change in personality in self-acceptance group
Reshape and split data set by intervention group:
Show the code
df_sbsa_wide_pers_sa_ppd <- df_sbsa %>%filter(rando=="Self-Acceptance") %>%arrange(pid, time) %>%select(pid, time, starts_with(c("sa01"))) %>%pivot_wider(names_from = time,names_sep ="_t",values_from =c(starts_with(c("sa01")))) %>%select(-c(sa01_01_t2, sa01_02_t2)) # colnames(df_sbsa_wide_pers_sa_ppd)group_assign <- df_sbsa %>%select(pid, rando) %>%unique()df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers %>%left_join(group_assign) %>%filter(rando=="Self-Acceptance") %>%select(-rando) %>%left_join(df_sbsa_wide_pers_sa_ppd)# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers_sa_ppd %>%mutate(ppd =rowMeans(across(c(sa01_01_t1, sa01_02_t1)), na.rm=T))
6.2.2.1 Big Five traits
Run models for all traits with a template & loop:
Show the code
# create templatestrait_template_ppd_accept <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingstrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ ppd # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ppd ~~ ppdppd ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal (6 = ideal)# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_ppd_accept, c("trait"= short_name,"ind01"=paste0(short_name, "_ideal_par1"), "ind02"=paste0(short_name, "_ideal_par2"), "ind03"=paste0(short_name, "_ideal_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), trait_model_fit))}
6.2.2.1.1 Extraversion: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the extraversion change score (ideal-self) is not significantly different from zero, b = -0.008, p = 0.75.
6.2.2.1.2 Agreeableness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the agreeableness change score (ideal-self) is not significantly different from zero, b = -0.019, p = 0.52.
6.2.2.1.3 Conscientiousness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the neuroticism change score (ideal-self) is not significantly different from zero, b = 0.007, p = 0.803.
6.2.2.1.5 Openness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the openness change score (ideal-self) is not significantly different from zero, b = -0.001, p = 0.956.
6.2.2.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific acceptance goalfacet_template_ppd_accept <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ppd # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ppd ~~ ppdppd ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[6]], item_nrs) template_filled <-str_replace_all(facet_template_ppd_accept, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), facet_model_fit))}
6.2.2.2.1 Sociability - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the sociability change score (ideal-self) is not significantly different from zero, b = -0.03, p = 0.326.
6.2.2.2.2 Assertiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the assertiveness change score (ideal-self) is not significantly different from zero, b = 0.008, p = 0.745.
6.2.2.2.3 Energy - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the energy change score (ideal-self) is not significantly different from zero, b = -0.003, p = 0.809.
6.2.2.2.4 Compassion - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the compassion change score (ideal-self) is not significantly different from zero, b = -0.002, p = 0.979.
6.2.2.2.5 Respectfulness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the respectfulness change score (ideal-self) is not significantly different from zero, b = -0.005, p = 0.855.
6.2.2.2.6 Trust - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the trust change score (ideal-self) is not significantly different from zero, b = 0.023, p = 0.477.
6.2.2.2.7 Organization - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the organization change score (ideal-self) is not significantly different from zero, b = 0.049, p = 0.068.
6.2.2.2.8 Productiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the productiveness change score (ideal-self) is not significantly different from zero, b = 0.038, p = 0.143.
6.2.2.2.9 Responsibility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the responsibility change score (ideal-self) is not significantly different from zero, b = -0.06, p = 0.123.
6.2.2.2.10 Anxiety - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the anxiety change score (ideal-self) is not significantly different from zero, b = -0.022, p = 0.67.
6.2.2.2.11 Depression - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the depression change score (ideal-self) is not significantly different from zero, b = -0.013, p = 0.51.
6.2.2.2.12 Volatility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the volatility change score (ideal-self) is not significantly different from zero, b = 0.003, p = 0.955.
6.2.2.2.13 Curiosity - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the curiosity change score (ideal-self) is not significantly different from zero, b = 0.028, p = 0.378.
6.2.2.2.14 Aesthetic - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the aesthetic change score (ideal-self) is not significantly different from zero, b = 0.005, p = 0.47.
6.2.2.2.15 Imagination - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):